All baseline methods provide the flexibility of changing the memory footprint of the trained model. In IF we can control the depth and number of trees; in DILOF window size and number of past windows can be controlled; in deep learning models like ONLAD, we can control the neural network size. However, for USAD, CAE-M, MAD-GAN and SlimGAN we can also control the parameter size factor provided by the model compression, CNN/GAN slimming or slimmable layers [1]}, [2]}, [3]}, [4]}. For GON, we only change the number of layers (keeping fixed layer size of 128 nodes). We choose the model memory footprint based on grid search by maximizing F1/GB. The motivation behind this metric is as follows. Increasing memory footprint leads to a monotonic increase in the anomaly detection performance (see Fig. REF ). However, when running online anomaly detection on edge devices, several other applications may be running alongside. Having the maximum possible F1 score would impact negatively the performance of the running applications due to memory contentions. However, having a low memory footprint model but with poor F1 score would be ineffective in detecting anomalies for apt remediation. Thus, a careful balance needs to be maintained to prevent memory contentions and provide high detection. This can be seen by Fig. REF  that shows the F1 and F1/GB scores of GON model on the SMD dataset. For the model with memory consumption of \(\sim \) 2.5GB the F1/GB is maximum and hence is used in our experiments. Similarly, we determine the compression and slimming factors in ONLAD, USAD, CAE-M, MAD-GAN and SlimGAN (see Fig. REF  for the MSDS dataset).
